diff --git a/examples/asr/speech_classification/vad_infer.py b/examples/asr/speech_classification/vad_infer.py
index 992096b6a6e1..8ab040b34c79 100644
--- a/examples/asr/speech_classification/vad_infer.py
+++ b/examples/asr/speech_classification/vad_infer.py
@@ -123,7 +123,7 @@ def main(cfg):
         f"Finish generating VAD frame level prediction with window_length_in_sec={cfg.vad.parameters.window_length_in_sec} and shift_length_in_sec={cfg.vad.parameters.shift_length_in_sec}"
     )
     frame_length_in_sec = cfg.vad.parameters.shift_length_in_sec
-    
+
     # overlap smoothing filter
     if cfg.vad.parameters.smoothing:
         # Generate predictions with overlapping input segments. Then a smoothing filter is applied to decide the label for a frame spanned by multiple segments.
diff --git a/nemo/collections/asr/parts/utils/vad_utils.py b/nemo/collections/asr/parts/utils/vad_utils.py
index 021e36744eda..074286a0805e 100644
--- a/nemo/collections/asr/parts/utils/vad_utils.py
+++ b/nemo/collections/asr/parts/utils/vad_utils.py
@@ -637,7 +637,7 @@ def generate_vad_segment_table_per_tensor(sequence: torch.Tensor, per_args: Dict
 
     speech_segments, _ = torch.sort(speech_segments, 0)
 
-    dur = speech_segments[:, 1:2] - speech_segments[:, 0:1] + 0.01 # 10ms frame unit
+    dur = speech_segments[:, 1:2] - speech_segments[:, 0:1] + 0.01  # 10ms frame unit
     speech_segments = torch.column_stack((speech_segments, dur))
 
     return speech_segments
diff --git a/scripts/voice_activity_detection/vad_tune_threshold.py b/scripts/voice_activity_detection/vad_tune_threshold.py
index a3b871e887f8..2f41677fe3ae 100644
--- a/scripts/voice_activity_detection/vad_tune_threshold.py
+++ b/scripts/voice_activity_detection/vad_tune_threshold.py
@@ -82,10 +82,7 @@
         default='DetER',
     )
     parser.add_argument(
-        "--frame_length_in_sec",
-        help="frame_length_in_sec ",
-        type=float,
-        default=0.01,
+        "--frame_length_in_sec", help="frame_length_in_sec ", type=float, default=0.01,
     )
     args = parser.parse_args()
 
@@ -131,7 +128,13 @@
         )
 
     best_threhsold, optimal_scores = vad_tune_threshold_on_dev(
-        params, args.vad_pred, args.groundtruth_RTTM, args.result_file, args.vad_pred_method, args.focus_metric, args.frame_length_in_sec
+        params,
+        args.vad_pred,
+        args.groundtruth_RTTM,
+        args.result_file,
+        args.vad_pred_method,
+        args.focus_metric,
+        args.frame_length_in_sec,
     )
     logging.info(
         f"Best combination of thresholds for binarization selected from input ranges is {best_threhsold}, and the optimal score is {optimal_scores}"